Evidence map›Paper›PMID 33402023›Full record

ArticleJournal of the Royal Society, Interface2021

Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling.

Alexander B Brummer, Panagiotis Lymperopoulos, Jocelyn Shen, Elif Tekin, Lisa P Bentley, Vanessa Buzzard, Andrew Gray, Imma Oliveras, Brian J Enquist, Van M Savage

Abstract read
In one paragraph

Article in Journal of the Royal Society, Interface, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Alexander B BrummerInstitute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.
Panagiotis LymperopoulosDepartment of Computer Science, Tufts University, Medford, MA, USA.
Jocelyn ShenDepartment of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Elif TekinInstitute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.
Lisa P BentleyDepartment of Biology, Sonoma State University, Rohnert Park, CA, USA.
Vanessa BuzzardSchool of Natural Resources and the Environment, University of Arizona, Tucson, AZ, USA.
Andrew GrayDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
Imma OliverasEnvironmental Change Institute, School of Geography and the Environment, University of Oxford, Oxford, UK.
Brian J EnquistDepartment of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA.
Van M SavageInstitute for Quantitative and Computational Biology, University of California, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Branching in vascular networks and in overall organismic form is one of the most common and ancient features of multicellular plants, fungi and animals. By combining machine-learning techniques with new theory that relates vascular form to metabolic function, we enable novel classification of diverse branching networks-mouse lung, human head and torso, angiosperm and gymnosperm plants. We find that ratios of limb radii-which dictate essential biologic functions related to resource transport and supply-are best at distinguishing branching networks. We also show how variation in vascular and branching geometry persists despite observing a convergent relationship across organisms for how metabolic rate depends on body mass.

Indexed as

Machine LearningPlantsAnimalsMathematicsbranching networksmachine learningmetabolic scalingvascular biology

Identifiers

PMID33402023
PMCPMC7879751

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.